Mapping of literature onto regions of interest on neurological images

ABSTRACT

A computer implemented method, apparatus, and computer program product for analyzing neurological images. A set of brain scans for a patient is compared to a set of baseline control scans to automatically identify regions of interest in the set of patient scans. A region of interest is an area in a scan that shows an indication of a potential abnormality. A set of electronic medical literature sources is searched for medical literature relevant to the regions of interest in the set of patient scans. The relevant medical literature is correlated to the medical literature describing the regions of interest in the set of patient scans to the regions of interest in the set of patient scans. A result is generated. The result comprises the regions of interest and a set of links to the correlated portions of the relevant medical literature are outputted.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related generally to a data processing systemand in particular to a method and apparatus for managingneuropsychiatric disease data. More particularly, the present inventionis directed to a computer implemented method, apparatus, and computerusable program code for automatically identifying regions of interest inbrain scans and mapping relevant portions of medical literature to thoseregions of interest.

2. Description of the Related Art

Neuropsychiatric disorders are disorders that have neurological featuresassociated with disorders of the nervous system, as well as psychiatricfeatures. Neuropsychiatric disorders may be treated using a variety oftherapies, such as talk therapy, behavioral therapy, chemical therapy,and/or mechanical therapy. Chemical therapy refers to pharmacotherapy,such as, the utilization of drugs. Mechanical therapy includeselectroconvulsive therapies (ECT). These therapies may be usedseparately or may be used in combination to treat patients diagnosedwith neuropsychiatric disorders.

However, many of these patients may not receive the most effectivetreatments due to difficulties in accurately diagnosing patients withneuropsychiatric disorders. Patients that are accurately diagnosed mayalso suffer from the side effects of both effective therapies and trailsof ineffective therapies. Furthermore, some patients may suffer foryears as a result of poorly understood disease phenotype, particularlyin cases involving the presentation of complex cases. In addition, whena disease is developing in a patient and the patient has not had asufficient number of “episodes” for diagnosis or has only manifested afew early stage symptoms, it may be difficult or impossible to clearlyand rapidly delineate a differential diagnosis.

BRIEF SUMMARY OF THE INVENTION

According to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product foranalyzing neurological images is provided. A neuroimage mapping managercompares a set of brain scans for a patient to a set of baseline controlscans to automatically identify a set of regions of interest in the setof patient scans. The set of brain scans for the patient may include aset of positron emission tomography scans of the patient and/or a set ofmagnetic resonance imaging scans of the patient. A region of interest isan area in a scan that shows an indication of a potential abnormality.The neuroimage mapping manager searches a set of electronic medicalliterature sources for medical literature relevant to the regions ofinterest in the set of patient scans to form relevant medicalliterature.

The neuroimage mapping manager correlates portions of the relevantmedical literature describing the set of regions of interest in the setof patient scans to the set of regions of interest in the set of patientscans to form correlated portions of the relevant medical literature.The portions of the relevant medical literature describing the set ofregions of interest in the set of patient scans comprises a set of brainscans in the relevant medical literature showing areas of a brain havingan identified abnormality that corresponds to the potential abnormalityshown in the set of regions of interest in the set of patient scans. Theneuroimage mapping manager then generates a result. The result comprisesthe set of patient scans and a set of links to the correlated portionsof the relevant medical literature.

In one embodiment, the neuroimage mapping manager presents the set ofregions of interest to a user. In response to receiving a selection of aset of additional regions of interest in the set of patient scans fromthe user to form a set of user selected regions of interest, theneuroimage mapping manager adds the set of user selected regions ofinterest to the set of regions of interest in the patient scans. Theneuroimage mapping manager automatically removes unselected regions fromthe set of regions of interest.

In another embodiment, searching the set of electronic medicalliterature sources further comprises identifying the set of electronicmedical literature sources; and searching medical literature availablefrom the set of electronic medical literature sources using at least oneof data mining, pattern recognition, search engines, queries to identifythe relevant medical literature in the medical literature available fromthe set of electronic medical literature sources, data mining cohort,pattern recognition cohort, search engine cohort, or any other cohortappliance of interest. A cohort is a group of one or more objects havinga common characteristic. For example, a data mining cohort may be,without limitation, a group of one or more objects associated withperforming data mining techniques to identify desired data from a datasource. A pattern recognition cohort may be, without limitation, a groupof pattern recognition software applications that identify patterns indata, such as medical data.

The set of baseline control scans comprises a set of baseline normalscans and/or a set of baseline abnormal scans. In response to receivinga set of brain scans for a set of healthy subjects in variousdemographic groups to form the baseline normal scans, the neuroimagemapping manager analyzes the baseline normal scans to identify a normalappearance of areas in normal brain scans, wherein a normal brain scanis a scan that does not show indications of disease or abnormalities inthe areas in the normal brain scans. In response to receiving a set ofbrain scans for a set of subjects in various demographic groups havingidentified abnormalities in the set of brain scans to form the baselineabnormal scans, the neuroimage mapping manager analyzes the baselineabnormal scans to identify an abnormal appearance of areas in brainscans, wherein an abnormal scan is a scan that shows indications ofdisease or abnormalities in the areas of the brain scans.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 is a block diagram of a neuroimage mapping manager in accordancewith an illustrative embodiment;

FIG. 4 is a block diagram of a magnetic resonance imaging brain scanhaving regions of interest in accordance with an illustrativeembodiment;

FIG. 5 is a positron emissions tomography brain scan having regions ofinterest in accordance with an illustrative embodiment;

FIG. 6 is a flowchart illustrating a process for analyzing a set ofbrain scans for a patient to identify regions of interest with links torelevant portions of the medical literature in accordance with anillustrative embodiment; and

FIG. 7 is a flowchart illustrating a process for generating baselinecontrol scans in accordance with an illustrative embodiment.

DETAILED DESCRIPTION OF THE INVENTION

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a system, method or computer program product.Accordingly, the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present invention may take the form of a computer program productembodied in any tangible medium of expression having computer usableprogram code embodied in the medium.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,device, or propagation medium. More specific examples (a non-exhaustivelist) of the computer-readable medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CDROM), an optical storage device, a transmission media such as thosesupporting the Internet or an intranet, or a magnetic storage device.Note that the computer-usable or computer-readable medium could even bepaper or another suitable medium upon which the program is printed, asthe program can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory. In the context of this document, a computer-usableor computer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

The present invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions.

These computer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer program instructions may also bestored in a computer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

FIG. 1 depicts a pictorial representation of a network of dataprocessing systems in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers in whichthe illustrative embodiments may be implemented. Network data processingsystem 100 contains network 102, which is the medium used to providecommunications links between various devices and computers connectedtogether within network data processing system 100. Network 102 mayinclude connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. Clients 110, 112, and 114 may be, for example,personal computers or network computers. In the depicted example, server104 provides data, such as boot files, operating system images, andapplications to clients 110, 112, and 114. Clients 110, 112, and 114 areclients to server 104 in this example. Network data processing system100 may include additional servers, clients, and other devices notshown.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as server104 or client 110 in FIG. 1, in which computer usable program code orinstructions implementing the processes may be located for theillustrative embodiments. In this illustrative example, data processingsystem 200 includes communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory206, in these examples, may be, for example, a random access memory orany other suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 also may be removable. For example, a removablehard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer recordable media218 may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 200 is anyhardware apparatus that may store data. Memory 206, persistent storage208, and computer readable media 218 are examples of storage devices ina tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

The illustrative embodiments recognize that some patients may sufferfrom the ineffective remediation and/or treatment of neuropsychiatricconditions using pharmaceuticals, as well as side effects of botheffective and ineffective therapies. Furthermore, many patients sufferfor years as a result of poorly understood disease phenotype,particularly in presentation of neuropsychiatric conditions that may notbe completely understood by the medical community. A neuropsychiatriccondition is a condition that involves neurological and/or psychiatricfeatures, such as, without limitation, depression, schizophrenia,bipolar disorder, and any other neuropsychiatric disorders.

Therefore, according to one embodiment of the present invention, acomputer implemented method, apparatus, and computer program product foranalyzing neurological images is provided. A neuroimage mapping managercompares a set of brain scans for a patient to a set of baseline controlscans to automatically identify set of regions of interest in the set ofpatient scans. The term “set” refers to one or more. The set of brainscans may include a single brain scan, as well as two or more brainscans. The set of brain scans for the patient may include a set ofpositron emission tomography scans of the patient and/or a set ofmagnetic resonance imaging scans of the patient. A region of interest isan area in a scan that shows an indication of a potential abnormality.The potential abnormality may include, without limitation, abnormalfunctionality, abnormal brain structure, a disease, an illness, braindamage, abnormal brain metabolism, or any other abnormality. Theneuroimage mapping manager searches a set of electronic medicalliterature sources for medical literature relevant to the set of regionsof interest in the set of patient scans to form relevant medicalliterature.

The neuroimage mapping manager correlates portions of the relevantmedical literature describing the set of regions of interest in the setof patient scans to the set of regions of interest in the set of patientscans to form correlated portions of the relevant medical literature.The portions of the relevant medical literature describing the set ofregions of interest in the set of patient scans comprises a set of brainscans in the relevant medical literature showing areas of a brain havingan identified abnormality that corresponds to the potential abnormalityshown in the set of regions of interest in the set of patient scans. Theneuroimage mapping manager then generates a result. The result comprisesthe set of patient scans and a set of links to the correlated portionsof the relevant medical literature.

In one embodiment, the neuroimage mapping manager presents the set ofregions of interest to a user. The regions of interest may be presentedin a visual format, an audio format, a combination of audio and visualformat, or any other format. In response to receiving a selection of anadditional region of interest in the set of patient scans from the userto form a user selected region of interest, the neuroimage mappingmanager adds the user selected region of interest to the regions ofinterest in the patient scans.

In another embodiment, searching the set of electronic medicalliterature sources further comprises identifying the set of electronicmedical literature sources; and searching medical literature availablefrom the set of electronic medical literature sources using at least oneof data mining, search engines, and queries to identify the relevantmedical literature in the medical literature available from the set ofelectronic medical literature sources.

The set of baseline control scans comprises a set of baseline normalscans and/or a set of baseline abnormal scans. In response to receivinga set of brain scans for a set of healthy subjects in variousdemographic groups to form the baseline normal scans, the neuroimagemapping manager analyzes the baseline normal scans to identify a normalappearance of areas in normal brain scans, wherein a normal brain scanis a scan that does not show indications of disease or abnormalities inthe areas in the normal brain scans. In response to receiving a set ofbrain scans for a set of subjects in various demographic groups havingidentified abnormalities in the set of brain scans to form the baselineabnormal scans, the neuroimage mapping manager analyzes the baselineabnormal scans to identify an abnormal appearance of areas in brainscans, wherein an abnormal scan is a scan that shows indications ofdisease or abnormalities in the areas of the brain scans.

FIG. 3 is a block diagram of a neuroimage mapping manager in accordancewith an illustrative embodiment. Neuroimage mapping manager 300 issoftware for analyzing patient brain scans to identify regions ofinterest in the brain scans and generate links to portions of interestin the medical literature. Computer 301 may be implemented in any typeof computing device, such as, without limitation, a server, a client, alaptop computer, a personal digital assistant (PDA), a smart phone, orany other known or available computing device.

Neuroimage analyzer 302 receives patient set of scans 304. Patient setof scans 304 is a set of one or more brain scans. Patient set of scans304 may include functional magnetic resonance imaging (FMRI) scans,structural magnetic resonance imaging (sMRI) scans, positron emissiontomography (PET) scans, or any other type of brain scans.

The scans in patient set of scans are generated by scanning device 305.Scanning device 305 may be implemented as a functional magneticresonance imaging device, a structural magnetic resonance imagingdevice, a positron emission tomography device, or any other type ofdevice for generating scans of a patient's brain. Scanning device 305saves the scans of the patient's brain in data storage device 306 aspatient set of scans 304.

Data storage device 306 may be implemented as a hard drive, a flashmemory, a main memory, read only memory (ROM), a random access memory(RAM), or any other type of data storage device. Data storage may beimplemented in a single data storage device or a plurality of datastorage devices. Data storage device 306 may be local to computer 301 orremote to computer 301.

Neuroimage analyzer 302 may receive the scans in patient set of scans304 from scanning device 305 as each scan is generated or neuroimageanalyzer 302 may retrieve the scans from a pre-generated set of scans,such as patient set of scans 304 in data storage device 306. Neuroimageanalyzer 302 analyzes patient set of scans 304 to identify regions ofinterest in the scans based on baseline normal scans and/or baselineabnormal scans for identified illnesses, abnormalities, diseases, ordisorders.

A region of interest is an area in a scan that shows an indication of apotential abnormality, a potential illness, a potential disease, apotential problem with metabolism, or any other deviation from whatwould be expected in a scan of the region for a healthy individualhaving similar characteristics as the patient. The similarcharacteristics may include, without limitation, an age range of thepatient, race, gender, or other factors influencing the development andappearance of regions of the brain in a scan.

Comparator 307 is a software component that compares patient set ofscans 304 to baseline normal scans 308 and/or baseline abnormal scans310. A baseline normal scans 308 is a set of one or more brain scans foraverage, healthy subjects having one or more characteristics in commonwith the patient. The characteristics in common may be any commoncharacteristic, such as, without limitation, age, gender, race,pre-existing conditions, profession, place of residence, nationality, orany other characteristic. For example, if the patient is a sixteen yearold female, baseline normal scans 308 may include scans of normal,healthy female subjects between the ages of fourteen and eighteen.

Comparator 307 compares one or more areas in each scan in patient set ofscans 304 with corresponding areas in one or more scans in baselinenormal scans 308 to identify areas of the patients scans that areconsistent with the scans of normal, healthy subjects and to identifyareas of the scans that are inconsistent with the scans of normal,healthy subjects. An area in a scan that is inconsistent with thecorresponding areas in baseline normal scans 308 are identified asregions of interest 312. A region identified in regions of interest 312may indicate a potential abnormality, illness, or condition. However,the regions in regions of interest 312 are not required to definitivelyindicate an abnormality, illness, condition, or other deviation from thenorm.

Baseline abnormal scans 310 is a set of one or more scans of subjectshaving one or more characteristics in common with the patient anddiagnosed with an identified condition. The identified condition may bea disease, an illness, a deformity, an abnormality, or any otheridentified deviation from the norm. For example, if the patient is amale, age thirty five, and diagnosed with diabetes, the baselineabnormal scans may include scans of male patients between the ages ofthirty and forty and having a variety of known neuropsychiatricdisorders. Comparator 307 compares regions in each scan in patient setof scans 304 with one or more scans in baseline abnormal scans 310 toidentify regions of interest in the patient's scans that showindications of neuropsychiatric disorders, illness, disease, orabnormalities. A region in a scan may show indications of a potentialillness, condition, abnormality, or neuropsychiatric disorder if theregion in the patient's scan is consistent with a corresponding regionin a brain scan of a subject having a known illness, condition,abnormality, or neuropsychiatric disorder.

Medical data and text analytics 314 is a software component forsearching a set of electronic medical literature sources for medicalliterature relevant to regions of interest 312 in patient set of scans304 and correlate portions of the relevant medical literature describingthe regions of interest in the set of patient scans to regions ofinterest 312.

Search engine 316 is any type of known or available informationretrieval software for locating medical literature that is relevant toregions of interest 312 in one or more sources. Search engine 316 may besoftware for searching data storage devices on a computer system or aweb search tool for searching for medical information on the World WideWeb. Search engine 316 may also make queries into databases, informationsystems, and other medical literature information sources to locateinformation relevant to regions of interest 312. Data mining 318 is asoftware tool for searching through information available from one ormore sources and retrieving medical information relevant to regions ofinterest 312.

Data mining 318, search engine 316, or any other software for locatingrelevant information may be used to search set of electronic medicalliterature sources 320 for relevant medical literature. Set ofelectronic medical literature sources 320 may include both onlinemedical literature sources that are accessed by computer 301 via anetwork connection, as well as off-line medical literature sources thatmay be accessed without a network connection. An example of anelectronic medical literature source includes, without limitation,PUBMED. Medical literature 322 is any literature, journal article,medical study results, medical text, pharmaceutical studies, or anyother medical information in an electronic format. Medical literature322 may include scans 324, such as magnetic resonance imaging scans,positron emission tomography scans, or any other type of brain scans.

Parser 326 is software for parsing medical literature 322 text into aform suitable for further analysis and processing. Parser 326 may beimplemented as any type of known or available parser. Correlation engine328 correlates portions of medical literature 332 with regions ofinterest 312. A portion of medical literature is a section of medicalliterature text and/or one or more scans that describe a region ofinterest, describe a condition, illness, deformity, abnormality,disease, or other cause for an appearance of a region of interest, anarea in a scan that is the same or similar to an area of interest, orotherwise is associated with a region of interest.

Neuroimage mapping manager 300 generates output 330, including regionsof interest 312 and portions of medical literature 332. Regions ofinterest 312 may be output with a set of links to portions of medicalliterature 332 embedded in set of scans 304 or embedded within regionsof interest 312. In another embodiment, links to portions of medicalliterature 332 may be output as results that are separate from patientset of scans 304. In another embodiment, the set of links to portions ofmedical literature 332 are embedded in an electronic medical file forthe patient. A user selects a link in the set of links to view a portionof medical literature associated with a region of interest. The portionof medical literature may be a scan only, text only, or a combination oftext and one or more scans. The portion of medical literature may be anentire or complete item, such as a complete medical journal article or acomplete section of a medical textbook. The portion of medicalliterature may also be a portion of a journal article, a portion of asection of a medical textbook, or other part of an item of medicalliterature.

In this embodiment, baseline normal scans 308 and baseline abnormalscans 310 are pre-generated and available for retrieval from datastorage device 306. However, in another embodiment, medical data andtext analytics 314 searches set of electronic medical literature sources320 for scans of normal, healthy subjects to create baseline normalscans 308. Medical data and text analytics 314 also searches set ofelectronic medical literature sources 320 for scans of subjects havingknown abnormalities, deformities, illnesses, ailments, diseases, orother neuropsychiatric disorders to create baseline abnormal scans 310.

Thus, neuroimage mapping manager 300 provides data and text analytics toautomatically determine regions of a patient's brain affected by diseaseas depicted in functional neuroimage data, such as functional magneticresonance imaging scans and positron emission tomography scans.Neuroimage mapping manager 300 applies technologies to data, such asheuristics, which automatically correlate the affected brain region withportions of medical literature 322 that describes the regions ofinterest 312 found in both functional and structural data in patient setof scans 304.

Input/output 334 may be implemented as any type of input and/or outputdevice for presenting regions of interest 312 to a user and/or receivinga selection of one or more regions of interest in patient set of scans304 from a user. In other words, neuroimage analyzer 302 automaticallyidentifies one or more regions of interest in patient set of scans 304.Neuroimage analyzer 302 may optionally present the automaticallyselected regions of interest to the user using input/output 334.

The automatically selected regions of interest may be presented using adisplay device to present the regions of interest in a visual format,using an audio device to present the regions of interest to the user inan audio format, using a combination of audio and visual devices, or anyother presentation device. The user may choose to select one or moreadditional regions of interest in patient set of scans 304. In such acase, neuroimage analyzer 302 adds the manually selected set of one ormore regions of interest to regions of interest 312. In anotherembodiment, the user may choose to de-select or remove one or moreregions of interest from the automatically selected regions of interest.In such a case, neuroimage analyzer 302 automatically removes the one ormore regions of interest selected for removal by the user from regionsof interest 312.

Referring to FIG. 4, a block diagram of a magnetic resonance imagingbrain scan having regions of interest is depicted in accordance with anillustrative embodiment. Scan 400 is a positron emission tomography scanof a brain of a patient. Scan 402 is a positron emission tomography scanof a normal, healthy subject. Scan 400 has regions of interest 404-408.Regions of interest 404-408 are areas in scan 400 that show indicationsof schizophrenia or any other abnormality. In this example, regions ofinterest 404-408 show disruptions in brain activity. Region 406 showsabnormal changes in the ventricles of the brain. Region 408 showsdecreased function in the frontal section.

Turning now to FIG. 5, a positron emissions tomography brain scan havingregions of interest is shown in accordance with an illustrativeembodiment. Scan 500 is a magnetic resonance imaging scan of a patient'sbrain. Scan 502 is a magnetic resonance imaging scan of a normal,healthy subject's brain. Scan 500 includes region of interest 504.Region 504 shows an abnormal enlargement of the ventricles of the brainwhen compared with scan 502 of a normal, healthy subject. Theenlargement of the ventricles shown in region of interest 504 mayindicate an illness or disease, such as, without limitation,schizophrenia. Therefore, a neuroimage mapping manager identifies region504 as a region of interest.

FIG. 6 is a flowchart illustrating a process for analyzing a set ofbrain scans for a patient to identify regions of interest with links torelevant portions of the medical literature in accordance with anillustrative embodiment. The process in FIG. 6 may be implemented bysoftware for analyzing patient brain scans to identify regions ofinterest in the brain scans and generate links to portions of interestin the medical literature, such as neuroimage mapping manager 300 inFIG. 3.

The neuroimage mapping manager receives a set of scans for a patient(step 602). The set of scans may include functional magnetic resonanceimaging (fMRI) scans, structural magnetic resonance imaging (sMRI)scans, positron emission tomography (PET) scans, or any other type ofbrain scans. The neuroimage mapping manager analyzes the set of scans toidentify regions of interest in the scans based on baseline normal scansand/or baseline abnormal scans for identified disorders (step 604). Theneuroimage mapping manager displays the identified regions of interestto a user (step 606). The neuroimage mapping manager makes adetermination as to whether a selection of one or more additionalregions of interest is received from the user (step 608).

If a selection of one or more additional regions of interest is receivedfrom the user, the neuroimage mapping manager adds the one or moreselected regions to the regions of interest (step 610). After adding theselected regions to the regions of interest at step 610 or if noselections of additional regions are received from the user at step 608,the neuroimage mapping manager retrieves relevant medical literaturefrom a set of sources using search engines, pattern recognition,queries, and/or data mining (step 612). The embodiments are not limitedto using only search engines, queries, and data mining. Any known oravailable method for locating desired information in an electronic datasource may be utilized.

Next, the neuroimage mapping manager identifies portions of interest inthe medical literature associated with and/or describing the regions ofinterest (step 614). The portions of interest may include pages,paragraphs, or portions of text describing one or more of the regions ofinterest, the appearance of one or more of the regions of interest, orthe characteristics of one or more of the regions of interest. Theportions of interest in the relevant medical literature may includeimages of scans containing one or more of the regions of interest,portions of text in the medical literature describing diseases,deficiencies, illnesses, and/or abnormalities that may cause theappearance of one or more of the regions of interest or one or morecharacteristics of the regions of interest, or any other portion ofmedical literature that is relevant to one or more of the regions ofinterest in the patient's scans. The neuroimage mapping manager outputsresults identifying the regions of interest with a set of links to theportions of interest in the medical literature (step 616) with theprocess terminating thereafter.

In this embodiment, the regions of interest are displayed to the userand the user is given an opportunity to select one or more additionalregions of interest to add to the regions of interest identified by theneuroimage mapping manager. In another embodiment, the regions ofinterest are not presented to the user prior to identifying the portionsof interest in the medical literature. In this embodiment, the user isnot required to review the regions of interest and provide input as towhether to add one or more additional regions of interest. In this case,the process may occur completely automatically without any user inputduring the process of analyzing the patient's scans to identify regionsof interest and linking portions of the relevant medical literature tothe regions of interest.

FIG. 7 is a flowchart illustrating a process for generating baselinecontrol scans in accordance with an illustrative embodiment. The processin FIG. 7 may be implemented by software for generating a set ofbaseline control scans, such as medical data and text analytics 314 inFIG. 3.

The process begins by searching a set of medical literature sources forscans of normal, healthy subjects (step 702). The scans of the normal,healthy subjects are saved in a data storage device to form baselinenormal scans (step 704). The process searches the set of medicalliterature sources for scans of subjects having known conditions (step706). The conditions may be a disease, an illness, an infection, adeformity, or any other condition. The scans of the subjects having theknown conditions are saved in the data storage device to form baselineabnormal scans (step 708) with the process terminating thereafter.

According to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product foranalyzing neurological images is provided. A neuroimage mapping managercompares a set of brain scans for a patient to a set of baseline controlscans to automatically identify regions of interest in the set ofpatient scans. A region of interest is an area in a scan that shows anindication of a potential abnormality. The neuroimage mapping managersearches a set of electronic medical literature sources for medicalliterature relevant to the regions of interest in the set of patientscans to form relevant medical literature. The neuroimage mappingmanager correlates portions of the relevant medical literaturedescribing the regions of interest in the set of patient scans to theregions of interest in the set of patient scans to form correlatedportions of the relevant medical literature. The neuroimage mappingmanager then outputs the regions of interest in the set of patient scansand a set of links to the correlated portions of the relevant medicalliterature.

The neuroimage mapping manager automates the assessment of neuroimagedata and literature to detect and document whether a disease process maybe occurring or whether a condition may be present. The neuroimagemapping manager improves the speed and potentially the accuracy ofdiagnostic and treatment processes. The neuroimage mapping managerautomates the determination of regions of interest in neuroimage datavia mapping of literature into magnetic resonance imaging scans orpositron emission tomography scans.

The neuroimage mapping manager lessens the workload on physicians andresearchers, permits more accurate data interpretation and analysis ofpatient scans, and allows physicians and researchers to more quicklyreach a diagnosis of neuropsychiatric conditions. In addition, theneuroimage mapping manager provides a decision support tool forclinicians in both clinical and research settings, to help themdetermine whether talk therapy, pharmacotherapy, or mechanicalelectroconvulsive therapy will be most effective by linking neuroimagedata with the relevant medical literature.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer implemented method of analyzing neurological images, thecomputer implemented method comprising: comparing a set of brain scansfor a patient to a set of baseline control scans to automaticallyidentify a set of regions of interest in the set of patient scans,wherein a region of interest is an area in a scan that shows anindication of a potential abnormality; searching a set of electronicmedical literature sources for medical literature relevant to the set ofregions of interest in the set of patient scans to form relevant medicalliterature; correlating portions of the relevant medical literaturedescribing the set of regions of interest in the set of patient scans tothe set of regions of interest in the set of patient scans to formcorrelated portions of the relevant medical literature; and generating aresult, wherein the result comprises the set of patient scans and a setof links to the correlated portions of the relevant medical literature.2. The computer implemented method of claim 1 wherein portions of therelevant medical literature describing the set of regions of interest inthe set of patient scans comprises a set of brain scans in the relevantmedical literature showing areas of a brain having an identifiedabnormality that corresponds to the potential abnormality shown in theset of regions of interest in the set of patient scans.
 3. The computerimplemented method of claim 1 further comprising: presenting the set ofregions of interest to a user; responsive to receiving a selection of aset of additional regions of interest in the set of patient scans fromthe user to form a set of user selected regions of interest, adding theset of user selected region of interest to the set of regions ofinterest in the set of patient scans; and responsive to receiving aselection of a set of undesired regions of interest in the set ofpatient scans from the user to form a set of de-selected regions ofinterest, removing the set of de-selected regions of interest from theset of regions of interest in the set of patient scans.
 4. The computerimplemented method of claim 1 wherein searching the set of electronicmedical literature sources further comprising: identifying the set ofelectronic medical literature sources; and searching medical literatureavailable from the set of electronic medical literature sources using atleast one of data mining, search engines, and queries to identify therelevant medical literature in the medical literature available from theset of electronic medical literature sources.
 5. The computerimplemented method of claim 1 wherein the set of baseline control scanscomprises a set of baseline normal scans, and further comprising:receiving a set of brain scans for a set of healthy subjects in variousdemographic groups to form the baseline normal scans; and analyzing thebaseline normal scans to identify a normal appearance of areas in normalbrain scans, wherein a normal brain scan is a scan that does not showindications of disease or abnormalities in the areas in the normal brainscans.
 6. The computer implemented method of claim 1 wherein the set ofbaseline control scans comprises a set of baseline abnormal scans, andfurther comprising: receiving a set of brain scans for a set of subjectsin various demographic groups having identified abnormalities in the setof brain scans to form the baseline abnormal scans; and analyzing thebaseline abnormal scans to identify an abnormal appearance of areas inbrain scans, wherein an abnormal scan is a scan that shows indicationsof disease or abnormalities in the areas of the brain scans.
 7. Thecomputer implemented method of claim 1 wherein the set of brain scanscomprises a set of positron emission tomography scans of the patient. 8.The computer implemented method of claim 1 wherein the set of brainscans comprises a set of magnetic resonance imaging scans of thepatient.
 9. The computer implemented method of claim 1 wherein the setof links to the correlated portions of the relevant medical literatureare embedded in the set of patient scans.
 10. A computer program productfor analyzing neurological images, the computer program productcomprising: a computer usable medium having computer usable program codeembodied therewith, the computer usable program code comprising:computer usable program code configured to compare a set of brain scansfor a patient to a set of baseline control scans to automaticallyidentify set of regions of interest in the set of patient scans, whereina region of interest is an area in a scan that shows an indication of apotential abnormality; computer usable program code configured to searcha set of electronic medical literature sources for medical literaturerelevant to the set of regions of interest in the set of patient scansto form relevant medical literature; computer usable program codeconfigured to correlate portions of the relevant medical literaturedescribing the set of regions of interest in the set of patient scans tothe set of regions of interest in the set of patient scans to formcorrelated portions of the relevant medical literature; and computerusable program code configured to generate a result, wherein the resultcomprises the set of patient scans and a set of links to the correlatedportions of the relevant medical literature.
 11. The computer programproduct of claim 10 wherein portions of the relevant medical literaturedescribing the set of regions of interest in the set of patient scanscomprises a set of brain scans in the relevant medical literatureshowing areas of a brain having an identified abnormality thatcorresponds to the potential abnormality shown in the set of regions ofinterest in the set of patient scans.
 12. The computer program productof claim 10 further comprising: computer usable program code configuredto present the set of regions of interest to a user; computer usableprogram code configured to add the set of user selected regions ofinterest to the set of regions of interest in the set of patient scansin response to receiving a selection of a set of additional regions ofinterest in the set of patient scans from the user to form a set of userselected regions of interest; and computer usable program codeconfigured to remove the set of de-selected regions of interest from theset of regions of interest in the set of patient scans in response toreceiving a selection of a set of undesired regions of interest in theset of patient scans from the user to form a set of de-selected regionsof interest.
 13. The computer program product of claim 10 whereinsearching the set of electronic medical literature sources furthercomprising: computer usable program code configured to identify the setof electronic medical literature sources; and computer usable programcode configured to search medical literature available from the set ofelectronic medical literature sources using at least one of data mining,search engines, and queries to identify the relevant medical literaturein the medical literature available from the set of electronic medicalliterature sources.
 14. The computer program product of claim 10 whereinthe set of baseline control scans comprises a set of baseline normalscans, and further comprising: computer usable program code configuredto receive a set of brain scans for a set of healthy subjects in variousdemographic groups to form the baseline normal scans; and computerusable program code configured to analyze the baseline normal scans toidentify a normal appearance of areas in normal brain scans, wherein anormal brain scan is a scan that does not show indications of disease orabnormalities in the areas in the normal brain scans.
 15. The computerprogram product of claim 10 wherein the set of baseline control scanscomprises a set of baseline abnormal scans, and further comprising:computer usable program code configured to receive a set of brain scansfor a set of subjects in various demographic groups having identifiedabnormalities in the set of brain scans to form the baseline abnormalscans; and computer usable program code configured to analyze thebaseline abnormal scans to identify an abnormal appearance of areas inbrain scans, wherein an abnormal scan is a scan that shows indicationsof disease or abnormalities in the areas of the brain scans.
 16. Thecomputer program product of claim 10 wherein the set of brain scanscomprises at least one of a set of positron emission tomography scans ofthe patient and a set of magnetic resonance imaging scans of thepatient.
 17. The computer program product of claim 10 wherein the set oflinks to the correlated portions of the relevant medical literature areembedded in the set of patient scans.
 18. An apparatus comprising: a bussystem; a communications system coupled to the bus system; a memoryconnected to the bus system, wherein the memory includes computer usableprogram code; and a processing unit coupled to the bus system, whereinthe processing unit executes the computer usable program code to comparea set of brain scans for a patient to a set of baseline control scans toautomatically identify a set of regions of interest in the set ofpatient scans, wherein a region of interest is an area in a scan thatshows an indication of a potential abnormality; search a set ofelectronic medical literature sources for medical literature relevant tothe set of regions of interest in the set of patient scans to formrelevant medical literature; correlate portions of the relevant medicalliterature describing the set of regions of interest in the set ofpatient scans to the set of regions of interest in the set of patientscans to form correlated portions of the relevant medical literature;and generate a result, wherein the result comprises the set of patientscans and a set of links to the correlated portions of the relevantmedical literature.
 19. The apparatus of claim 18 wherein the processorunit further executes the computer usable program code to identify theset of electronic medical literature sources; and search medicalliterature available from the set of electronic medical literaturesources using at least one of data mining, search engines, and queriesto identify the relevant medical literature in the medical literatureavailable from the set of electronic medical literature sources.
 20. Theapparatus of claim 18 wherein the processor unit further executes thecomputer usable program code to receive a set of brain scans for a setof subjects in various demographic groups having identifiedabnormalities in the set of brain scans to form the baseline abnormalscans; and analyze the baseline abnormal scans to identify an abnormalappearance of areas in brain scans, wherein an abnormal scan is a scanthat shows indications of disease or abnormalities in the areas of thebrain scans.
 21. The apparatus of claim 18 wherein the processor unitfurther executes the computer usable program code to receive a set ofbrain scans for a set of healthy subjects in various demographic groupsto form the baseline normal scans; and analyze the baseline normal scansto identify a normal appearance of areas in normal brain scans, whereina normal brain scan is a scan that does not show indications of diseaseor abnormalities in the areas in the normal brain scans.
 22. Anapparatus for mapping medical literature onto neurological images, theapparatus comprising: a neuroimage mapping manager, wherein theneuroimage mapping manager performs an analysis on neurological imagesof a patient and automatically identifies a set of regions of interestand maps relevant portions of medical literature to the set of regionsof interest, the neuroimage mapping manager further comprising: acomparator, wherein the comparator compares a set of brain scans for apatient to a set of baseline control scans to automatically identify setof regions of interest in the set of patient scans, wherein a region ofinterest is an area in a scan that shows an indication of a potentialabnormality; a medical data and text analytics component, wherein themedical data and text analytics component searches a set of electronicmedical literature sources for medical literature relevant to the set ofregions of interest in the set of patient scans to form relevant medicalliterature, and wherein the medical data and text analytics componentfurther comprises: a correlation engine, wherein the correlation enginecorrelates portions of the relevant medical literature describing theset of regions of interest in the set of patient scans to the set ofregions of interest in the set of patient scans to form correlatedportions of the relevant medical literature; and wherein the neuroimagemapping manager generates a result of the analysis of the neurologicalimages, wherein the result comprises the set of regions of interest inthe set of patient scans and a set of links to the correlated portionsof the relevant medical literature.
 23. The apparatus of claim 22further comprising: a set of computer, wherein the neuroimage mappingmanager is located on the set of computers.
 24. The apparatus of claim22 further comprising: an input/output device, wherein the input/outputdevice presents the set of regions of interest automatically selected bythe neuroimage mapping manager to a user and prompts the user toindicate whether the user desires to select one or more additionalregions of interest to be added to the set of regions of interest orselect one or more undesired regions of interest to be removed from theset of regions of interest; and wherein the neuroimage mapping manageradds the one or more additional regions of interest to the set ofregions of interest in the set of patient scans in response to theinput/output device receiving a selection of at least one additionalregion of interest from the user, and wherein the neuroimage mappingmanager removes de-selected regions of interest from the set of regionsof interest in response to receiving a selection of a set of de-selectedregions of interest to be removed from the set of regions of interestfrom the user.
 25. The apparatus of claim 22 wherein the medical dataand text analytics analyzes baseline normal scans to identify a normalappearance of areas in normal brain scans in response to receiving a setof brain scans for a set of healthy subjects in various demographicgroups to form the baseline normal scans and, wherein a normal brainscan is a scan that does not show indications of disease orabnormalities in the areas in the normal brain scans; and wherein themedical data and text analytics analyzes baseline abnormal scans toidentify an abnormal appearance of areas in brain scans in response toreceiving a set of brain scans for a set of subjects in variousdemographic groups having identified abnormalities in the set of brainscans to form the baseline abnormal scans, wherein an abnormal scan is ascan that shows indications of disease or abnormalities in the areas ofthe brain scans.